Open Access
Issue |
BIO Web Conf.
Volume 115, 2024
2nd Edition of the International Conference on “Natural Resources and Sustainable Development” (RENA23)
|
|
---|---|---|
Article Number | 01005 | |
Number of page(s) | 8 | |
Section | Satellite Remote Sensing for an Effective Natural Resource Management | |
DOI | https://doi.org/10.1051/bioconf/202411501005 | |
Published online | 25 June 2024 |
- D.D.R. Lageson, Introduction to Field Mapping of Geologic Structures. [Google Scholar]
- M.A. El-Omairi and A. El Garouani, A Review on Advancements in Lithological Mapping Utilizing Machine Learning Algorithms and Remote Sensing Data, H, 9, p. e20168, (Sep. 2023). [Google Scholar]
- C. Von Hagke, F. Wellmann, and J.L. Urai, Understanding Structures and Generating Geological Maps using Google Earth, Chapter 14 -From Google Earth to 3D Geology Problem 1, A. Billi and Å. Fagereng, Eds, P&SSGT, 5, Elsevier, pp. 181–188, (2019). [Google Scholar]
- J. Hartmann and N. Moosdorf, The New Global Lithological Map Database GLiM: A Representation of Rock Properties at the Earth Surface, GGG, 12, (2012). [Google Scholar]
- G.H. Brimhall, J.H. Dilles, and J.M. Proffett, The Role of Geologic Mapping in Mineral Exploration, in Wealth Creation in the Minerals Industry: Integrating Science, Business, and Education, MDD& JRPE.SOEG, 12, (2005). [Google Scholar]
- B. Janga, G.P. Asamani, Z. Sun, and N. Cristea, A Review of Practical AI for Remote Sensing in Earth Sciences, RS, 16, Art. no. 16, (Jan. 2023). [Google Scholar]
- L. Yu, A. Porwal, E.-J. Holden, and M.C. Dentith, Towards Automatic Lithological Classification from Remote Sensing Data Using Support Vector Machines, CG, 45, pp. 229–239, (Aug. 2012). [Google Scholar]
- J.R. Harris and E.C. Grunsky, Predictive Lithological Mapping of Canada’s North Using Random Forest Classification Applied to Geophysical and Geochemical Data, CG, 80, pp. 925, (Jul. 2015). [Google Scholar]
- H. Shirmard, E. Farahbakhsh, E. Heidari, A. Beiranvand Pour, B. Pradhan, D. Müller and R. Chandra, M.A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data, RS, 4, p. 819, (Feb. 2022). [Google Scholar]
- M.J. Cracknell and A.M. Reading, Geological Mapping Using Remote Sensing Data: A Comparison of Five Machine Learning Algorithms, Their Response to Variations in the Spatial Distribution of Training Data and the Use of Explicit Spatial Information, CG, 63, pp. 22–33, (Feb. 2014). [Google Scholar]
- W. Han, X. Zhang, Y. Wang, L. Wang, X. Huang, J. Li, S. Wang, W. Chen, X. Li, R. Feng, R. Fan, X. Zhang and Y. Wang. A Survey of Machine Learning and Deep Learning in Remote Sensing of Geological Environment: Challenges, Advances, and Opportunities, ISPRS JPRS, 202, pp. 87–113, (Aug. 2023). [Google Scholar]
- K. Benshili, Lias-Dogger du Moyen Atlas Plissé (Maroc): Sédimentologie, Biostratigraphie et Évolution Paléogéographique, Thèse de Doctorat, Lyon 1, (1987). [Google Scholar]
- C. Hoepffner, Le Massif Paléozoïque du Tazekka (Maroc): Analyse des Déformations Liées à un Linéament Tectonique, SGBM, 1, pp. 33–44, (1978). [Google Scholar]
- S. Hinaje, M.E. Fartati, D. Yaagoub, S. Amrani, Y. Gharmane, and B.E.F. Idrissi, Paleocontraintes et Contexte Tectonique de Mise en Place du Volcanisme Alcalin Néogène et Quaternaire du Moyen Atlas (Maroc), ESJESJ, 15, (May 2019). [Google Scholar]
- N. Saoud, I. Derkaoui, J. Choukrad, M.A.E. Moussalim, and M. Charroud, Contribution of X-Ray Diffraction in the Identification of Crystalline Phases of the Mineralization Hosted in the Mesozoic Cover of the Tazzeka Hercynian Massif- Maghrawa Region - Morocco, UJG, 3, pp. 54–67, (Oct. 2020). [CrossRef] [Google Scholar]
- A. Shebl, T. Kusky, and Á. Csámer, Advanced Land Imager Superiority in Lithological Classification Utilizing Machine Learning Algorithms, AJG15, 9, p. 923, (May 2022). [Google Scholar]
- B. Bahrambeygi and H. Moeinzadeh, Comparison of Support Vector Machine and Neural Network Classification Method in Hyperspectral Mapping of Ophiolite Mélanges-A Case Study of East ofIran, Egypt. JRSSS, 1, pp. 1–10, (Jun. 2017). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.